---
title: Qdrant 
---

## Code

```python cookbook/knowledge/vector_db/qdrant_db/qdrant_db.py
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant

COLLECTION_NAME = "thai-recipes"

vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")

contents_db = PostgresDb(
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    knowledge_table="knowledge_contents",
)

knowledge = Knowledge(
    name="My Qdrant Vector Knowledge Base",
    description="This is a knowledge base that uses a Qdrant Vector DB",
    vector_db=vector_db,
    contents_db=contents_db,
)

knowledge.add_content(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
)


agent = Agent(knowledge=knowledge)
agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)


vector_db.delete_by_name("Recipes")

vector_db.delete_by_metadata({"doc_type": "recipe_book"})
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install libraries">
    ```bash
    pip install -U qdrant-client pypdf openai agno
    ```
  </Step>

  <Step title="Run Qdrant">
    ```bash
    docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
    ```
  </Step>

    <Step title="Run Agent">
    <CodeGroup>
    ```bash Mac
    python cookbook/knowledge/vector_db/qdrant_db/qdrant_db.py
    ```

    ```bash Windows
    python cookbook/knowledge/vector_db/qdrant_db/qdrant_db.py
    ```
    </CodeGroup>
  </Step>
</Steps>